# Fast and Accurate Importance Weighting for Correcting Sample Bias

arXiv: https://arxiv.org/abs/2209.04215 · HTML: https://mlsanity.com/paper/2209.04215/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 9 Sep 2022.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [MMD GAN: Towards Deeper Understanding of Moment Matching Network](https://mlsanity.com/paper/1705.08584/) (2017)
- [Rethinking Importance Weighting for Deep Learning under Distribution Shift](https://mlsanity.com/paper/2006.04662/) (2020)
- [Importance Weighted Adversarial Nets for Partial Domain Adaptation](https://mlsanity.com/paper/1803.09210/) (2018)
- [Learning to Transfer Examples for Partial Domain Adaptation](https://mlsanity.com/paper/1903.12230/) (2019)
- [Partial Transfer Learning with Selective Adversarial Networks](https://mlsanity.com/paper/1707.07901/) (2017)
- [Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection](https://mlsanity.com/paper/2103.00236/) (2021)
- [Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation](https://mlsanity.com/paper/2003.00343/) (2020)
- [Transferable Calibration with Lower Bias and Variance in Domain Adaptation](https://mlsanity.com/paper/2007.08259/) (2020)
- [Bayesian Uncertainty Matching for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1906.09693/) (2019)
- [Adversarial Weighting for Domain Adaptation in Regression](https://mlsanity.com/paper/2006.08251/) (2020)
- [ADAPT : Awesome Domain Adaptation Python Toolbox](https://mlsanity.com/paper/2107.03049/) (2021)
- [Nearest Neighbor-based Importance Weighting](https://mlsanity.com/paper/2102.02291/) (2021)
- [Importance Weighted Generative Networks](https://mlsanity.com/paper/1806.02512/) (2018)

## Built on by (most cited first, 5 of 5)

- [SwiftLearn: A Data-Efficient Training Method of Deep Learning Models using Importance Sampling](https://mlsanity.com/paper/2311.15134/) (2023)
- [Domain Adaptation-Enhanced Searchlight: Enabling classification of brain states from visual perception to mental imagery](https://mlsanity.com/paper/2408.01163/) (2024)
- [Domain Adaptation Framework for Turning Movement Count Estimation with Limited Data](https://mlsanity.com/paper/2503.20113/) (2025)
- [Some notes concerning a generalized KMM-type optimization method for density ratio estimation](https://mlsanity.com/paper/2309.07887/) (2023)
- [A Practical Upper Bound on Selection Bias Effects in Medical Prediction Models](https://mlsanity.com/paper/2606.00563/) (2026)

## Abstract (arXiv)

Bias in datasets can be very detrimental for appropriate statistical estimation. In response to this problem, importance weighting methods have been developed to match any biased distribution to its corresponding target unbiased distribution. The seminal Kernel Mean Matching (KMM) method is, nowadays, still considered as state of the art in this research field. However, one of the main drawbacks of this method is the computational burden for large datasets. Building on previous works by Huang et al. (2007) and de Mathelin et al. (2021), we derive a novel importance weighting algorithm which scales to large datasets by using a neural network to predict the instance weights. We show, on multiple public datasets, under various sample biases, that our proposed approach drastically reduces the computational time on large dataset while maintaining similar sample bias correction performance compared to other importance weighting methods. The proposed approach appears to be the only one able to give relevant reweighting in a reasonable time for large dataset with up to two million data.